A Comparison of Short and Medium Range Statistical Forecasting Methods

Abstract
Exponential Smoothing, Moving Average, and Least Squares forecasting models were tested by simulating their operation on seven years of actual data for various sewing machine product groups. The relative accuracy of the forecasts varied according to the length of the period being forecasted and the characteristics of the data. Tests were also conducted on synthetic series designed to isolate the cyclical, trend and noise components. For the series tested, the Exponential Smoothing and Moving Average methods were about equal in overall performance for intermediate range forecasts (next six months' demand). For the short range (next month's demand), the Exponential Smoothing gave slightly better over-all results. The difference in relative performance between the Exponential Smoothing and Moving Average methods for intermediate versus short range forecasts appears to be due to a subcomponent identified as "caused noise."

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